This project is currently being completely rebuilt from scratch. The entire logic will be reworked using a more optimized, compiled backend.
Projects based on the library in its current state are still planned, however !!
pip install open-taranis --upgradeFor package on PyPi
or
git clone https://github.com/SyntaxError4Life/open-taranis && cd open-taranis/ && pip install .For last version
Simplest
import open_taranis as T
client = T.Clients.openrouter # API_KEY in env_var
request = T.Request(
tools=None, tool_choice="auto",
temperature=0.4,
# and others....
)
print("assistant : ",end="")
for token, is_thinking, tools, tool_bool, meta in T.handle_streaming(
request=request,
client=client,
model="nvidia/nemotron-3-nano-30b-a3b:free",
messages=[T.create.user_prompt("Tell me about yourself")],
API_KEY=None
) :
# You can add `if not is_thinking :` to see only the reals tokens
print(token, end="", flush=True)
print(f"\n\n{meta}")Make a simple agent with a context windows on the 6 last turns
import open_taranis as T
class Agent(T.agent_base):
def __init__(self):
super().__init__(yield_thinking=False) # If you want to return the reasoning
self.client = T.Clients.openrouter
self._system_prompt = [T.create.system_prompt(
"You're an agent nammed **Taranis** !"
)]
self.request_profil = T.Request() # Useful for highly customized clients like Venice.ai
def create_stream(self, history):
return T.handle_streaming(
self.request_profil,
self.client,
model="nvidia/nemotron-3-nano-30b-a3b:free",
messages= self._system_prompt + history # Most important !
)
def manage_token_yield(self, token, is_thinking = None, meta = None, tool_calls = None):
return token, meta # You can customize what the agent returns
def manage_messages(self):
self.messages = self.messages[-12:] # Each turn have 1 user and 1 assistant
My_agent = Agent()
while True :
prompt = T.create.user_prompt(input("user : "))
print("\n\nagent : ", end="")
for t, meta in My_agent(prompt):
print(t, end="", flush=True)
print(f"\n\n{meta}\n","="*60,"\n")To create a simple display using gradio as backend
import open_taranis as T
import open_taranis.web_front as W
import gradio as gr
class Gradio_agent(T.agent_base):
def __init__(self):
super().__init__()
self._system_prompt = [T.create_system_prompt("You are a agent nammed **Taranis**")]
def manage_token_yield(self, token, is_thinking):
return token, is_thinking
def create_stream(self):
return T.clients.openrouter_request(
client=T.clients.openrouter(),
messages=self._system_prompt+self.messages,
model="nvidia/nemotron-3-nano-30b-a3b:free"
)
gr.ChatInterface(
fn=W.create_fn_gradio(Gradio_agent()),
title="Open-taranis Agent"
).launch()Here we use a temporary history provided with each request via Agent(user_prompt=...., temporary_history=messages), so it is natively supported for concurrency (no persistent memory in the object).
taranis help: in the name...taranis update: upgrade the framework
Available in French Soon
- v0.0.1: start
- v0.0.x: Add and confirm other API providers (in the cloud, not locally)
- v0.1.x: Functionality verifications in examples
- v0.2.x: Add features for logic-only coding approach, start with
agent_base - v0.3.x: Complete rewrite + Add proper documentation and improved deployments
- v0.4.x: Improving support for local AI deployment
- The rest will follow soon.
v0.0.x : The start
- v0.0.4 : Add xai and groq provider
- v0.0.6 : Add huggingface provider and args for clients.veniceai_request
v0.1.x : Gradio, commands and TUI
- v0.1.0 : Start the docs, add update-checker and preparing for the continuation of the project...
- v0.1.1 : Code to deploy a frontend with gradio added (no complex logic at the moment, ex: tool_calls)
- v0.1.2 : Fixed a display bug in the web_front and experimentally added ollama as a backend
- v0.1.3 : Fixed the memory reset in the web_front and remove ollama module for openai front (work 100 times better)
- v0.1.4 : Fixed
web_frontfor native use on huggingface, as well ashandle_streamingwhich had tool retrieval issues - v0.1.7 : Added a TUI and commands, detection of env variables (API keys) and tools in the framework
v0.2.x : Agents
- v0.2.0 : Adding
agent_base - v0.2.1 : Updated
agent_baseand added a more concrete example of agents - v0.2.2 : Upgraded all the code to add Kimi Code as client and reduce code (Not official !)
- v0.2.3 : Updated
agent_base, add some functions and add a cool agent - v0.2.4 : Improved CoT techniques and updated
web_front.py, deploy an agent to the browser in a few lines
v0.3.x : The restart
- v0.3.0 : Rewrite all the code from scratch (without AI) to improve everything
- v0.3.1 : The TUI project with integrated agent has been removed to focus on the framework (useful code).
- v0.3.2 (future) : Add features for coding MCP servers and clients